化工学报2011，Vol.62Issue(8)：2345-2349,5.DOI:10.3969/j.issn.0438-1157.2011.08.045
改进的双模型结构RBF神经网络及其应用
Improved RBF neural network with double model structure and its application
摘要
Abstract
A dual model RBF (radial basis function) neural network was proposed in this paper. One is used for self-learning, which learns one time a day. The other is used for on-line correcting, which is the running model currently. Both the self-learning model and the on-line correcting model are corrected six times every day and should track the current conditions of the system quickly. At the same time, the accuracy of the two models should be compared. If the accuracy of the on-line correcting model is less than the one of the self-learning model, the latter becomes the new currently running model instead of the old one. Otherwise, the currently model is maintained. To solve the problem of neural network large prediction errors, a network algorithm analysis is given and the influence factors of the network prediction accuracy are found. At last, an improved algorithm of RBF neural network modeling is proposed, which combines K-means clustering method with the recursive descent algorithm. Simulation and practical application proved the effectiveness of the improved method.关键词
RBF神经网络/软仪表/双模型结构Key words
RBF neural networks software instrument/ double model分类
信息技术与安全科学引用本文复制引用
李全善,张义山,曹柳林,林晓琳,崔佳..改进的双模型结构RBF神经网络及其应用[J].化工学报,2011,62(8):2345-2349,5.基金项目
国家自然科学基金项目(60974031,60704011) （60974031,60704011）
北京市中小企业创新基金项目(Z09010400260912). （Z09010400260912）
